Panoramica dell'architettura di un sistema RAG
Comprenda i componenti e il flusso di lavoro di un tipico sistema RAG, evidenziando il ruolo dei database vettoriali.
Panoramica dell'architettura di un sistema RAG è una lezione Vector Databases: Pinecone, Weaviate & pgvector gratuita su CoddyKit. Questa è la lezione 1 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento Vector Databases: Pinecone, Weaviate & pgvector, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso Vector Databases: Pinecone, Weaviate & pgvector include 4 lezioni in totale.
Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.
What is RAG?
Welcome! In this lesson, we'll explore Retrieval Augmented Generation (RAG) systems. RAG is a powerful technique that combines large language models (LLMs) with external knowledge sources.
It allows LLMs to generate more accurate, up-to-date, and context-rich responses by retrieving relevant information before generating an answer. Think of it as giving an LLM a personal research assistant!
LLM's Knowledge Gap
Large Language Models (LLMs) are amazing, but they have limitations:
- Knowledge Cutoff: Their training data is static, so they don't know about recent events or information.
- Hallucinations: They can sometimes generate plausible-sounding but factually incorrect information.
- Domain Specificity: They lack deep knowledge about private, proprietary, or highly specialized data.
RAG helps address these challenges by providing real-time, relevant facts.
How RAG Bridges the Gap
RAG introduces an information retrieval step before the LLM generates its response. Instead of relying solely on its internal training, the LLM is given specific context from an external knowledge base.
This means the LLM can answer questions about new data, company documents, or specific topics it wasn't originally trained on, significantly reducing hallucinations and improving factual accuracy.
Core RAG Components
A RAG system typically consists of several key components working together:
- Knowledge Base: Your source documents.
- Embedding Model: Converts text to numerical vectors.
- Vector Database: Stores and indexes these vectors.
- Retriever: Finds relevant information from the vector database.
- Generator (LLM): Uses the retrieved info to form an answer.
Let's look at each part in more detail.
The Knowledge Base
The knowledge base is the foundation of your RAG system. It's where all the information you want your LLM to access resides.
This can include:
- Company documents (PDFs, internal wikis)
- Web articles or blogs
- Books or research papers
- Databases or structured data
The quality and relevance of this data directly impact the RAG system's performance.
Embedding & Indexing
Before data can be searched, it needs to be processed. This involves two main steps:
- Chunking: Breaking down large documents into smaller, manageable pieces (chunks).
- Embedding: Using an embedding model to convert each text chunk into a numerical vector (an embedding). These vectors capture the semantic meaning of the text.
These embeddings are then stored and indexed for efficient retrieval.
The Vector Database
This is where the 'vector' in RAG comes in! A vector database is specialized to store and efficiently search these high-dimensional vector embeddings.
When a user asks a question, the query is also converted into an embedding. The vector database then quickly finds the most 'similar' (closest in vector space) document chunks to that query.
The Retriever Component
The retriever is the part of the RAG system responsible for fetching relevant context from your knowledge base.
When a user submits a query:
- The query is embedded.
- The retriever uses this embedding to search the vector database.
- It returns the top-K (e.g., top 3 or 5) most similar text chunks.
These retrieved chunks are the 'context' that will be passed to the LLM.
The Generator (LLM)
Finally, the generator, which is your Large Language Model (LLM), takes over. Instead of just the user's query, it receives both the query AND the retrieved context.
It then synthesizes this information to formulate a comprehensive and accurate answer. Try this simple conceptual Python example:
def generate_response(query, context):
# This function simulates how an LLM uses context.
# In a real RAG, a complex LLM API call would happen here.
prompt = f"""Based on the following context, answer the question.
Context: {context}
Question: {query}
Answer:"""
# Simulate LLM processing
if "capital of France" in query.lower() and "Paris" in context:
return "The capital of France is Paris, according to the context provided."
else:
return f"LLM would process: '{prompt}' and generate a thoughtful response based on the context."
if __name__ == "__main__":
user_query = "What is the capital of France?"
retrieved_context = "Paris is the capital and most populous city of France, located on the Seine River."
print("--- RAG Process Simulation ---")
print(f"User Query: {user_query}")
print(f"Retrieved Context: {retrieved_context}")
llm_response = generate_response(user_query, retrieved_context)
print(f"LLM Response: {llm_response}")RAG System Workflow
Let's put it all together. Here's the typical flow when a user queries a RAG system:
- User Query: A user asks a question.
- Embed Query: The query is converted into an embedding.
- Retrieve Context: The embedding is used to search the vector database for relevant document chunks.
- Augment Prompt: The original query is combined with the retrieved context to create an enriched prompt.
- Generate Response: This augmented prompt is sent to the LLM, which generates the final answer.
Quick Check: RAG Flow
Which of the following steps happens *before* the Large Language Model (LLM) generates its final response in a RAG system?
RAG: Recap & Next Steps
Great job! You've learned the fundamental architecture of a RAG system. We covered:
- Why RAG is needed to overcome LLM limitations.
- The core components: Knowledge Base, Embedding Model, Vector Database, Retriever, and Generator (LLM).
- The step-by-step workflow from user query to LLM response.
Understanding this architecture is key to building powerful, context-aware AI applications. Next, we'll dive into integrating RAG with popular LLM frameworks!
Domande Frequenti
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Tutte le lezioni di questo corso
- Panoramica dell'architettura di un sistema RAG
- Integrare i framework LLM
- Recupero di informazioni contestuali
- Strategie di suddivisione dei chunk per RAG